Interoperable biometric representation

Abstract

A process for interoperable biometric representation can include receiving a biometric representation in a first format. The process can include determining a dimension parameter based on the biometric representation, wherein the dimension parameter does not exceed a dimension of the biometric representation. The process can include generating a common biometric representation in a second format by applying a feature-to-feature mapping function to the biometric representation, wherein a vector dimension of the common biometric representation equals the dimension parameter. The process can include applying a lossy transformation to the common biometric representation to generate a token.

What is claimed is: 
     
       1. A process, comprising:
 receiving a biometric representation in a first format; 
 determining a dimension parameter based on the biometric representation, wherein the dimension parameter does not exceed a dimension of the biometric representation; 
 generating a common biometric representation in a second format by applying a feature-to-feature mapping function to the biometric representation, wherein the feature-to-feature mapping function is based on the dimension parameter and comprises a deep neural network; 
 training the deep neural network on a training dataset comprising a plurality of mated and non-mated biometric images associated with a plurality of human subjects; and 
 applying a lossy transformation to the common biometric representation to generate a token. 
 
     
     
       2. The process of  claim 1 , wherein a vector dimension of the token is less than the dimension parameter. 
     
     
       3. A process, comprising:
 receiving a biometric representation in a first format; 
 determining a dimension parameter based on the biometric representation, wherein the dimension parameter does not exceed a dimension of the biometric representation; 
 generating a common biometric representation in a second format by applying a feature-to-feature mapping function to the biometric representation, wherein the feature-to-feature mapping function is based on the dimension parameter and comprises a deep neural network; 
 generating a training dataset comprising a plurality of mated and non-mated synthetic biometric images, wherein the training dataset excludes biometric data associated with real human subjects; and 
 training the deep neural network on the training dataset. 
 
     
     
       4. The process of  claim 3 , wherein sets of mated biometric images of the training dataset each comprise at least one biometric image associated with an optimal condition and at least one biometric image associated with a non-optimal condition. 
     
     
       5. The process of  claim 4 , wherein the non-optimal condition is an underlit lighting condition. 
     
     
       6. The process of  claim 4 , wherein the non-optimal condition is an adverse backlight condition. 
     
     
       7. The process of  claim 4 , wherein the non-optimal condition is an overlit lighting condition. 
     
     
       8. The process of  claim 4 , wherein the non-optimal condition is a rotation condition. 
     
     
       9. The process of  claim 4 , wherein:
 the plurality of mated and non-mated synthetic biometric images comprise facial images; 
 the optimal condition is a first facial expression; and 
 the non-optimal condition is a second facial expression different from the first facial expression. 
 
     
     
       10. The process of  claim 3 , further comprising applying a lossy transformation to the common biometric representation to generate a token. 
     
     
       11. The process of  claim 10 , wherein a vector dimension of the token is less than the dimension parameter. 
     
     
       12. A system, comprising:
 at least one processor in communication with at least one data store; 
 the at least one data store comprising:
 a feature-to-feature mapping function that, when applied, transforms biometric representations from a first format to a common format; and 
 a dimensionality reduction function that, when applied, reduces a dimension of biometric representations in the common format to a dimension parameter; 
 
 a non-transitory, machine-readable memory device comprising instructions stored thereon that, when executed by the at least one processor, cause the at least one processor to:
 obtain a first biometric representation in the first format; 
 obtain a second biometric representation in the common format, wherein the first biometric representation is associated with a first subject and the second biometric representation is associated with a second subject; 
 determine the dimension parameter for the common format based on the first biometric representation and the second biometric representation, wherein the dimension parameter does not exceed a vector size of the first biometric representation or the second biometric representation; 
 apply the feature-to-feature mapping function to the first biometric representation to generate a first common biometric representation; 
 apply the dimensionality reduction function to the second biometric representation to generate a second common biometric representation, wherein the first common biometric representation and the second common biometric representation are of a second vector size equal to the dimension parameter; 
 compare the first common biometric representation to the second common biometric representation; 
 based on the comparison, determine that the first common biometric representation is within a similarity threshold of the second common biometric representation; and 
 transmit, to a computing device, a positive verification of a match between the first subject and the second subject. 
 
 
     
     
       13. The system of  claim 12 , wherein:
 the feature-to-feature mapping function comprises a deep neural network; and 
 the instructions, when executed by the at least one processor, further cause the at least one processor to train the deep neural network on a first training dataset comprising a plurality of mated and non-mated biometric representations associated with human subjects. 
 
     
     
       14. The system of  claim 13 , wherein the instructions, when executed by the at least one processor, further cause the at least one processor to:
 generate a second training dataset comprising a plurality of mated and non-mated synthetic biometric representations; and 
 train the deep neural network on the second training dataset. 
 
     
     
       15. The system of  claim 14 , wherein the instructions, when executed by the at least one processor, further cause the at least one processor to:
 generate a third training dataset comprising at least a portion of the first training dataset and the second training dataset; and 
 train the deep neural network on the third training dataset. 
 
     
     
       16. A non-transitory, computer-readable medium comprising instructions that, when executed by a computer, cause the computer to:
 obtain a first common biometric representation of a first length and in a first format; 
 obtain a second biometric representation of a second length and in a second format, wherein the first length exceeds the second length; 
 apply a feature-to-feature mapping function to the second biometric representation to transform the second biometric representation into a second common biometric representation in the first format, wherein the second common biometric representation comprises a third length less than the first length and the second length; and 
 apply a dimensionality reduction function to the first common biometric representation to reduce the first common biometric representation from the first length to the third length. 
 
     
     
       17. The non-transitory, computer-readable medium of  claim 16 , wherein the instructions, when executed by the computer, cause the computer to apply a lossy transformation to each of the first common biometric representation and the second common biometric representation to generate a first token and a second token. 
     
     
       18. The non-transitory, computer-readable medium of  claim 17 , wherein the instructions, when executed by the computer, cause the computer to positively verify an identity of a subject associated with the second biometric representation based on a comparison between the first token and the second token.